Technological uncertainty and superstardom: two sources of inequality within occupations Peter B. Meyer Office of Productivity and Technology, U.S. Bureau of Labor Statistics Jan 9, 2004 SGE session, ASSA meetings, Philadelphia

Subject: effect of tech adaptation Earnings dispersion has risen in U.S. since 1973, and there is some relation to new information technology There is also a general historical question about whether and how new technologies affect earnings inequality Hypotheses in this paper: – tech uncertainty raises earnings inequality [yes] –superstars effect [yes] –other occupations feel this some too –but nurturing and must-be-present physical work do not

Tech uncertainty A state in which people don’t agree on forecasts of the future technology of production. –Technology is changing Discussed by Dosi (1988), Rosenberg (1996). It follows from this condition that efforts to experiment with the technology are chancy. They are gambles. If they are gambles, this condition is a source of noise in productivity, profits, asset prices, and wages.

superstars discussion If there are a thousand villages each with one musician and the market for each musician is his own village, that market supports musicians all making modest wages. Now suppose there is an improvement in technology so one can buy packaged music and musicians can travel. The market size has expanded and now the musicians are competing with one another and some will be at the top (“stars”) and some at the bottom and this raises income inequality among musicians. Modeled by Rosen (1981) If an occupation has the property that each person in it provides services which are distinct from one another (imperfect substitutability) AND This performance can be consumed jointly by many customers at once (e.g., because it’s shown on TV) THEN Expanding markets mean more inequality because small advantages at the top have a big effect on market share.

tech uncertainty and change ee’s and programmers. what do they do? operating definition of tech uncertainty: “occupations that involve working with novel, incomplete, or malfunctioning computer systems”

New in this time period ( ): EEs and computer programmers were inventing and buffeted by these inventions. They affect a lot the opportunities availab.e these guys are creating tech change and also buffeted by it. they don’t have a lot of choice abou that. so it’s not just a furnace, it’s also a casino. of tech uncert. e.g. bill gates, larry ellison, paul allen, steve jobs, notice skill bias doesn’t forecast bill gates well. HardwareSoftware Disk drivesGUI (icons, dropdown menus) Semiconductor memory Computer mice MicroprocessorsElectronic spreadsheets Bit-mapped video IOWeb Internet hardwareE-commerce MicrocomputersWeb search

graphs of ee and programmers inequality fast graphs of other engineers superstars graphs Not doctors and lawyers – note that doctors are very quantified. graph of amplified regression with high tech vs superstars vs other vs nurturing

Note that the effects we’re talking about don’t seem closely related to USE of computers, per table from Note that the EE’s are doing us all a service by adapting truly new technology. they are going right where the unknowns are, seeking disequilibrium situations. if we can systematize that we have a kind of economic measure of the existence of novelty and uncertainty. notice just by observation that some economies respond quickly to technological change compared to others – and this kind of adaptation may be essential to being quick about it.

this helps us document that the work of adapting to moore’s law and other radical technological changes is detectable and stressful. implications to theory: there exists models of wage or price dispersion in which workers vary in ability/skill/competence. an alternative here is that the environment is noisy. I’m trying to establish the statistics without biasing too much regarding the theory.

New tools and materials // FPGA – Verilog Sample Code 5: // Write Operations in IDT Standard Mode // // Description: Single Write Operation of the FIFO in IDT Standard mode with the // // assumption that the FIFO is not full. Note that the architecture of the FIFO will // // automatically prevent further reads from the FIFO when the device is full. // // Port definition going to the input port of the TeraSync. // input[35:0] dataport; // Register definition for write operations // reg[35:0] data_reg; (posedge WCLK) begin if (FF == 1) // Ensure the device is not full // begin state <= write_operation; case (state) write_operation: begin dataport <= data_reg // data reg is an assigned register of // // 36 bits which stores the data to be // // written into the FIFO. // write_enable <= 0; // Set write enable active to write data // write_chip_sel <= 0; // Enable write port for data output // state <= write_operation; end endcase; else if (FF == 0) // Device is empty, disable write port // begin write_enable <= 1; // Disable write enable when FIFO is full //write_chip_select <= 1; // Write port can be either enabled or // // disabled. // end module flip_flop(clock, din, dout, set, reset); input clock, din, set, reset; output dout; reg dout; clock or set or reset) begin if(!reset) dout <= #(0) 1'b0; else if(!set) dout <= #(0) 1'b1; else dout <= #(0) din; end endmodule

New programming languages object-oriented languages concept of programming in hardware rapid price declines price volatility opportunities therefore appear and disappear Graph of Moore’s Law

Extended occupational category system We wish to make comparisons of aggregate observations about occupational categories over time but the census/cps categories change every ten years. so we try to map the others to the 1990 system, analogous to what the IPUMS project did. (Meyer and Osborne paper) this turns out to be a substantial project, not finished.